machine_learning

Final Project for ML in Python

In this project, We will complete a notebook where you will build a classifier to predict whether a loan case will be paid off or not.

We load a historical dataset from loan applications, clean the data, and apply different classification algorithm on the data. You are expected to use the following algorithms to build your models:

k-Nearest Neighbour
Decision Tree
Support Vector Machine
Logistic Regression

The results is reported as the accuracy of each classifier, using the following metrics when these are applicable:

Jaccard index
F1-score
LogLoass

Keras.ipynb

In this we will build a regression model using the deep learning Keras library, and then we will experiment with increasing the number of training epochs and changing number of hidden layers and we will see how changing these parameters impacts the performance of the model.

For your convenience, the data can be found here again: https://cocl.us/concrete_data. To recap, the predictors in the data of concrete strength include:

Cement
Blast Furnace Slag
Fly Ash
Water
Superplasticizer
Coarse Aggregate
Fine Aggregate

Pytorch

In this lab, we will use pre-trained models to classify between the negative and positive samples; we will be provided with the dataset object. The particular pre-trained model will be resnet18; we will have three questions:

change the output layer
train the model
identify several misclassified samples

ResNet 50

In this we will build another classifier using another pre-trained model, namely the VGG16 model, following the same steps we completed in the previous module {Pytorch} using the ResNet50 pre-trained model. Then you will test the classifiers on a test set.

Apache-Spark

In this we will analyze a real-world dataset and apply machine learning on it using Apache Spark.